---
title: "COVID-19 no Brasil"
author: "Carlos Eduardo Veras Neves (adaptado de Antoine Soetewey)"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus")
library(coronavirus)
data(coronavirus)
update_datasets()
# View(coronavirus)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Brazil") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))
df_daily <- coronavirus %>%
  dplyr::filter(Country.Region == "Brazil") %>%
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(active = confirmed - death - recovered) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    recovered_cum = cumsum(recovered),
    active_cum = cumsum(active)
  )
df1 <- coronavirus %>% dplyr::filter(date == max(date))
```

Sumário
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$confirmed),  big.mark = ".", decimal.mark =  ","), "", sep = " "),
  caption = "Número total de casos confirmados",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```


### active {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$unrecovered, na.rm = TRUE), big.mark = ".", decimal.mark =  ","), " (",
    round(100 * sum(df$unrecovered, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Casos ativos (% do total de casos)", icon = "fas fa-ambulance",
  color = active_color
)
```

### death {.value-box}

```{r}
valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ".", decimal.mark =  ","), " (",
    round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Mortes (taxa de mortalidade)",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


Row
-----------------------------------------------------------------------

### **Número de casos acumulados por tipo** (Brasil somente)
    
```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(
    x = ~date,
    y = ~active_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Ativos",
    line = list(color = active_color),
    marker = list(color = active_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Mortes",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-02-26"),
    y = 1,
    text = paste("Primeiro caso"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-17"),
    y = 3,
    text = paste("Primeira morte"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = 90,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-16"),
    y = 14,
    text = paste(
      "Medidas de",
      "
",
      "contenção"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -20,
    ay = -90
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Número acumulado de casos"),
    xaxis = list(title = "Data"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Números comparados
=======================================================================


Column {data-width=400}
-------------------------------------


### **Novos casos diários**
    
```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(date >= "2020-02-29") %>%
  dplyr::mutate(country = Country.Region) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(cases)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total)



#----------------------------------------
# Plotting the data
daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~Brazil,
    type = "scatter",
    mode = "lines+markers",
    name = "Brasil"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~France,
    type = "scatter",
    mode = "lines+markers",
    name = "França"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Spain,
    type = "scatter",
    mode = "lines+markers",
    name = "Espanha"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Italy,
    type = "scatter",
    mode = "lines+markers",
    name = "Itália"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Número de novos casos confirmados (por dia)"),
    xaxis = list(title = "Data"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```




### **Distribuição dos casos por tipo**

```{r daily_summary}
df_EU <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Brazil" |
    Country.Region == "France" |
    Country.Region == "Italy" |
    Country.Region == "Spain") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
  data = df_EU,
  x = ~country,
  y = ~unrecovered,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Casos Ativos",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Mortes",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total de casos"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```


Previsões para o Brasil
=======================================================================


Row {data-width=400}
-----------------------------------------------------------------------


### R0 {.value-box}

```{r}
library(deSolve)

Infected = daily_confirmed$Brazil[daily_confirmed$Brazil>0] #incidência
N = 211000000 # população Brasileira
Day <- 1:(length(Infected))

init <- c(S = N-Infected[1], I = Infected[1], R = 0)

RSS <- function(parameters) {
  names(parameters) <- c("beta", "gamma")
  out <- ode(y = init, times = Day, func = SIR, parms = parameters)
  fit <- out[ , 3]
  sum((Infected - fit)^2)
}

SIR <- function(time, state, parameters) {
  par <- as.list(c(state, parameters))
  with(par, {
    dS <- -beta/N * I * S
    dI <- beta/N * I * S - gamma * I
    dR <- gamma * I
    list(c(dS, dI, dR))
  })
}

Opt <- optim(c(0.5, 0.5), RSS, method = "L-BFGS-B", lower = c(0, 0), upper = c(1, 1)) # optimize with some sensible conditions
#Opt$message
## [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"

Opt_par <- setNames(Opt$par, c("beta", "gamma"))

## Warning in xy.coords(x, y, xlabel, ylabel, log = log): 1 y value <= 0
## omitted from logarithmic plot

t <- 1:90 # time in days
fit <- data.frame(ode(y = init, times = t, func = SIR, parms = Opt_par))
col <- 1:3 # colour

R0 <- setNames(Opt_par["beta"] / Opt_par["gamma"], "R0")

##       R0 
## 2.073224

pico = fit[fit$I == max(fit$I), "I", drop = FALSE] # height of pandemic
##            I
## 50 232001865

max_mortes = max(fit$I) * 0.02 # max deaths with supposed 2% fatality rate
## [1] 4640037



valueBox(
  value = paste(format(round(R0,2), decimal.mark =  ",")
  ),
  caption = "Taxa Básica de Reprodução (R0)", icon = "fas fa-user-md",
  color = active_color
)




```

### infectados_pico {.value-box}

```{r}


valueBox(
  value = paste(format(pico, big.mark = ".", decimal.mark =  ",")," (",
    round(100 * pico / N, 1),
    "%)",
    sep = ""
  ),
  caption = "Número máximo de infectados", icon = "fas fa-ambulance",
  color = active_color
)




```

### mortes_pico {.value-box}

```{r}


valueBox(
  value = paste(format(max_mortes, big.mark = ".", decimal.mark =  ",")," (",
    round(100 * max_mortes / N, 1),
    "%)",
    sep = ""
  ),
  caption = "Número de mortos", icon = "fas fa-heart-broken",
  color = active_color
)




```


Column {data-width=400}
-------------------------------------


### **Modelo de regressão linear para os novos casos diários (escala logarítmica)  **
    
```{r}
library(zoo)

daily_confirmed10 <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(date >= "2020-02-29") %>%
  dplyr::mutate(country = Country.Region) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = log(sum(cases))) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total)

#----------------------------------------
# linear fit for data in log scale
fit_br = lm(Brazil ~ date, data = daily_confirmed10, subset=Brazil>0)
fit_sp = lm(Spain ~ date, data = daily_confirmed10, subset=Spain>0)
fit_it = lm(Italy ~ date, data = daily_confirmed10, subset=Spain>0)
fit_fr = lm(France ~ date, data = daily_confirmed10, subset=France>0)


#----------------------------------------
# Plotting the data
daily_confirmed10 %>%
  plotly::plot_ly() %>%
  #plotly::layout(yaxis = list(type = "log")) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Brazil,
    type = "scatter",
    mode = "markers",
    name = "Brasil",
    color=I('blue')
  ) %>%
  plotly::add_lines(
    x = ~date[as.numeric(names(fit_br$fitted.values))],
    y = fitted(fit_br),
    name="Brasil reg.linear",
    color=I('blue')
    ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~France,
    type = "scatter",
    mode = "markers",
    name = "França",
    color=I('green')
  ) %>%
  plotly::add_lines(
    x = ~date[as.numeric(names(fit_fr$fitted.values))],
    y = fitted(fit_fr),
    name="França reg.linear",
    color=I('green')
    ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Spain,
    type = "scatter",
    mode = "markers",
    name = "Espanha",
    color=I('black')
  ) %>%
  plotly::add_lines(
    x = ~date[as.numeric(names(fit_sp$fitted.values))],
    y = fitted(fit_sp),
    name="Espanha reg.linear",
    color=I('black')
    ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Italy,
    type = "scatter",
    mode = "markers",
    name = "Itália",
    color=I('red')
  ) %>%
  plotly::add_lines(
    x = ~date[as.numeric(names(fit_it$fitted.values))],
    y = fitted(fit_it),
    name="Itália reg.linear",
    color=I('red')
    ) %>%  
  
  plotly::layout(
    title = "",
    legend = list(x = .8, y = 0.1),
    yaxis = list(title = "Número de novos casos confirmados (por dia) - Log"),
    xaxis = list(title = "Data"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```

### **Modelo de Previsão (SIR) para o Brasil (população de 211 milhões)** 


```{r }
#matplot(fit$time, fit[ , 2:4], type = "l", xlab = "Day", ylab = "Number of subjects", lwd = 2, lty = 1, col = col)
matplot(fit$time, fit[ , 2:4], type = "l", xlab = "Day", ylab = "Número de pessoas", lwd = 2, lty = 1, col = col, log = "y")
## Warning in xy.coords(x, y, xlabel, ylabel, log = log): 1 y value <= 0
## omitted from logarithmic plot

points(Day, Infected)
legend("bottomright", c("Suscetíveis", "Infectados", "Recuperados"), lty = 1, lwd = 2, col = col, inset = 0.05)
title("SIR modelo 2019-nCoV Brasil", outer = TRUE, line = -2)



```



Mapa
=======================================================================

### **Mapa mundial dos casos** (*use + e - para dar zoom in/out*)

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
  # dplyr::filter(Country.Region == "Belgium") %>%
  dplyr::filter(cases > 0) %>%
  dplyr::group_by(Country.Region, Province.State, Lat, Long, type) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::mutate(log_cases = 2 * log(cases)) %>%
  dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk(function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(
        data = cv_data_for_plot.split[[df]],
        lng = ~Long, lat = ~Lat,
        #                 label=~as.character(cases),
        color = ~ pal(type),
        stroke = FALSE,
        fillOpacity = 0.8,
        radius = ~log_cases,
        popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
          feature.id = FALSE,
          row.numbers = FALSE,
          zcol = c("type", "cases", "Country.Region", "Province.State")
        ),
        group = df,
        #                 clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
        labelOptions = labelOptions(
          noHide = F,
          direction = "auto"
        )
      )
  })
map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```